code: def soft_update(self, target, source):
      for target_param, param in zip(
          target.parameters(), source.parameters()
      ):
          target_param.data.copy_(
              target_param.data * (1.0 - self.tau)
              + param.data * self.tau
          )
  
imports: import numpy
  import torch
  import torch.nn
  from torch.optim import Adam
  from .memory import SequentialMemory
  from .utils import to_numpy, to_tensor
label_desc: Use image node_id 11 for calling the DDPG obj's underlying member method code with example usage: obj.soft_update(target, source) without return types